RT Journal
A1 Ha, Gavin
A1 Roth, Andrew
A1 Khattra, Jaswinder
A1 Ho, Julie
A1 Yap, Damian
A1 Prentice, Leah M
A1 Melnyk, Nataliya
A1 McPherson, Andrew
A1 Bashashati, Ali
A1 Laks, Emma
A1 Biele, Justina
A1 Ding, Jiarui
A1 Le, Alan
A1 Rosner, Jamie
A1 Shumansky, Karey
A1 Marra, Marco A
A1 Gilks, C Blake
A1 Huntsman, David G
A1 McAlpine, Jessica N
A1 Aparicio, Samuel
A1 Shah, Sohrab P
T1 TITAN: Inference of copy number architectures in clonal cell populations from tumor whole genome sequence data
JF Genome Research 
JO Genome Research 
YR 2014 
FD July 24 
DO 10.1101/gr.180281.114 
SP gr.180281.114 
UL http://genome.cshlp.org/content/early/2014/07/24/gr.180281.114.abstract 
AB The evolution of cancer genomes within a single tumor creates mixed cell populations with divergent somatic mutational landscapes.  Inference of tumor subpopulations has been disproportionately focused on the assessment of somatic point mutations, whereas computational methods targeting evolutionary dynamics of copy number alterations (CNA) and loss of heterozygosity (LOH) in whole genome sequencing data remain under-developed.  We present a novel probabilistic model, TITAN, to infer CNA and LOH events while accounting for mixtures of cell populations, thereby estimating the proportion of cells harboring each event.  We evaluate TITAN on idealized mixtures, simulating clonal populations from whole genome sequences taken from genomically heterogeneous ovarian tumor sites collected from the same patient.  In addition, we show in 23 whole genomes of breast tumors that inference of CNA and LOH using TITAN critically inform population structure and the nature of the evolving cancer genome.  Finally, we experimentally validated subclonal predictions using fluorescence in situ hybridization (FISH) and single-cell sequencing from an ovarian cancer patient sample, thereby recapitulating the key modeling assumptions of TITAN.